Overview

As a Full Stack Developer with a passion for innovation, I recently had the opportunity to work on two dynamic AI/Machine Learning projects. These projects allowed me to utilize my diverse skill set across the development stack, encompassing everything from designing intuitive user interfaces to creating complex backend architectures, training machine learning models, and deploying solutions on VPS servers.

Project 1: PoseNet Kick Tracking

Objective

The goal of this project was to build a robust kick-tracking system capable of accurately detecting and analyzing front kick movements in video inputs. This system is particularly valuable for applications in fitness, martial arts training, and sports performance analysis.

Key Features and Approaches

1. Machine Learning Integration
   • Leveraged PoseNet, a state-of-the-art pose estimation model, to extract key skeletal points from video frames.
   • Trained the system to recognize and differentiate between various movements, focusing specifically on identifying front kick patterns.
2. Video Processing and Analysis
   • Implemented algorithms to process video frames in real-time, ensuring smooth and accurate tracking of movements.
   • Developed an analysis module that provided insights into movement accuracy, speed, and angles.
3. Full Stack Development
   • Frontend: Designed an intuitive user interface that allowed users to upload videos, view kick tracking in real-time, and access detailed movement analysis reports.
   • Backend: Built a scalable backend system to process video uploads, run ML inference, and store analysis results in a secure database.
4. Deployment on VPS Servers
   • Deployed the complete solution on a Virtual Private Server (VPS) to ensure accessibility and performance.
   • Optimized the system for efficient computation, enabling real-time analysis while minimizing latency.

Challenges and Solutions

• Challenge: Ensuring high accuracy in detecting complex movements across various lighting and camera angles.
  • Solution: Fine-tuned the PoseNet model using a curated dataset of front kick movements and applied data augmentation techniques to improve robustness.
• Challenge: Real-time processing of video frames on resource-constrained environments.
  • Solution: Implemented optimized pre-processing techniques and lightweight ML inference frameworks to enhance performance.

Impact

This project demonstrated the potential of machine learning in motion analysis, delivering a powerful tool for users in sports and fitness domains. It combined cutting-edge AI technologies with practical deployment strategies to provide a seamless and effective solution.

Conclusion

The PoseNet Kick Tracking project was an exciting journey that pushed the boundaries of what AI and machine learning can achieve in real-world applications. By integrating technical expertise with creative problem-solving, this project stands as a testament to my ability to handle challenging and innovative development tasks end-to-end.